In contrast to classical neural networks – which learn mappings from a set of inputs to a set of outputs – stochastic neural networks (SNNs) learn mappings from a set of inputs to a set of probability distributions over the set of outputs. The added complexity introduced by the stochasticity exacerbates the study of their theoretical properties.
Stochastic neural networks have several advantages with respect to deterministic DNNs: they have greater expressive power as they allow for
multi-modal mappings, and the stochasticity can be seen as added regularization [1]}. In [2]}, the authors point out that the main goal of using SNNs is to obtain a better idea of the uncertainty associated with the underlying process.
